The ability to classify various sub-areas in a field represented by an image, e.g., an aerial image, could be useful for various purposes, such as the classification of areas according to the land uses.
Foresters, in particular, are often required to inventory their plantations in order to quantify the volume of wood and project future wood supply upon harvest. The inventory is usually conducted by sampling plots in the actual forest, where performing statistical extrapolation of data collected in the plots produces the forest inventory estimation.
There are various methods of designing the forest samples, some using fixed and others variable, areas. For forest inventory, circular and rectangular plots are commonly used for sampling. The ratio between the areas of the samples and the population the samples represent serve as the statistical estimator for calculations.
Plotting the actual sample areas in the forest may consume significant time and labor. Moreover, when the timber population of interest is an artificial plantation that has well defined planting rows, it is likely that area sampling units show high variability among themselves and hence result in inaccurate estimates of population parameters. This could produce relatively inaccurate timber inventory.
Therefore, it may be beneficial to use row-segments as the sample units. In such a case, the estimator for calculation can be the ratio between the sample linear length and the total linear length of planted rows of the represented population. This could be beneficial in providing easier movement along planting rows and saving the time and effort of defining aerial samples across rows. The results are likely to be more accurate due to the lower sample unit to sample unit variability, which could also allow smaller samples for an equivalent statistical accuracy.
Such a method may be applicable to any plantation, including, but not limited to orchards, vines, citrus, forests, etc. Such a method may also be applicable to any rows-based agricultural application and/or any application of positioning items in an order based on rows, either straight or curved. In cases where planting is conducted using automated planting machines that use GPS (Global Positioning System) equipment, the recording of the planting machine's path could produce the planting rows map, and enable their length measurement. However, this method is only applicable for some plantations and mandate recording of planting rows during plantation. For existing plantations, such information often does not exist.
One alternative way of obtaining the total length of the represented population might be the measurement of planted rows in the forest. However, this method involves significant manual labor and may not be economically feasible. In cases where an aerial image of the plantation exists, image analysts could manually detect and mark the plantation rows, thereby enabling their length measurement. However, this too involves significant manual labor and might not be economically beneficial. Manual methods are also prone to human errors.
There is therefore a need in the art for a system and method to enable accurate and efficient row-identification over large plantation areas.